Method, apparatus and computer program for generating sports game highlight video based on excitement of gameplay

ABSTRACT

A method for generating a sports game highlight video based on excitement of gameplay includes receiving video of the sports game from at least one image capture device; performing object tracking analysis on the video to generate tracking data providing object trajectory and player position information; identifying shot events in the video of the sports game based on said tracking data; extracting video segments of the identified shot events from the video of the sports game; generating shot event metadata for indexing the video segments; calculating a point excitement matrix by a point excitement model based on said shot event metadata; and generating a sports game highlight video based on said point excitement matrix.

FIELD OF THE DISCLOSURE

This application claims the benefit of United Kingdom Patent ApplicationNo. 2202466.5, filed Feb. 23, 2022, the disclosure of which isincorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

The present technique relates to a method, apparatus and computerprogram for video processing of sports game recording.

DESCRIPTION OF THE RELATED ART

The “background” description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thebackground section, as well as aspects of the description which may nototherwise qualify as prior art at the time of filing, are neitherexpressly or impliedly admitted as prior art against the presenttechnique.

In a sports game tournament involving matches between multiple players,a vast amount of video recordings could result and it is often desirablefor spectators to have a summary video of the tournament to highlightthe best moments in each match, whether it is the longest rally in agame, a perfectly struck topspin lob ending a long deuce, the final shotof a five-time champion to defend his/her title, or a player coming backto triumph after losing the first two sets. In another scenario when twoor more matches are in progress at the same time, a spectator watching alive broadcast of one of the matches may wish to glimpse anyparticularly exciting moments in other matches. It is thereforedesirable for the live broadcast system to identify these excitingmoments in real time.

The identification of the most exciting moments in the matches areperformed manually by experienced commentators. However, this process istime consuming and inefficient, which cannot satisfy the needs ofproviding real-time highlight during the matches or immediate summary atthe end of the matches.

It is an aim of embodiments of the present disclosure to at leastaddress this issue.

SUMMARY

According to the disclosure, there is provided a method for generating asports game highlight video based on excitement of gameplay, comprisingthe steps of: receiving video of the sports game from at least one imagecapture device; performing object tracking analysis on the video togenerate tracking data providing object trajectory and player positioninformation; identifying shot events in the video of the sports gamebased on said tracking data; extracting video segments of the identifiedshot events from the video of the sports game; generating shot eventmetadata for indexing the video segments; calculating a point excitementmatrix by a point excitement model based on said shot event metadata;and generating a sports game highlight video based on said pointexcitement matrix.

The foregoing paragraphs have been provided by way of generalintroduction, and are not intended to limit the scope of the followingclaims. The described embodiments, together with further advantages,will be best understood by reference to the following detaileddescription taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the disclosure and many of the attendantadvantages thereof will be readily obtained as the same becomes betterunderstood by reference to the following detailed description whenconsidered in connection with the accompanying drawings, wherein:

FIG. 1 describes a video processing system for generating sports gamehighlight video based on quality of gameplay according to embodiments ofthe disclosure;

FIG. 2 describes the shot event analyser of FIG. 1 according toembodiments of the disclosure;

FIG. 3 describes the winning probability model of FIG. 1 according toembodiments of the disclosure;

FIG. 4 describes a database structure for the database in FIG. 1 whichstores player historical statistics according to embodiments of thedisclosure;

FIG. 5 describes an exemplary dataset of a winning probability matrixaccording to embodiments of the disclosure;

FIG. 6 describes the point level probability model in FIG. 3 accordingto embodiments of the disclosure;

FIG. 7 describes the game level probability model in FIG. 3 according toembodiments of the disclosure;

FIG. 8 describes the set level probability model in FIG. 3 according toembodiments of the disclosure;

FIG. 9 describes a neural network for winning probability modelaccording to embodiments of the disclosure;

FIG. 10 describes the point excitement model of FIG. 1 according toembodiments of the disclosure;

FIG. 11 describes an exemplary dataset of a point excitement matrixaccording to embodiments of the disclosure;

FIG. 12 describes a neural network for point excitement model accordingto embodiments of the disclosure; and

FIG. 13 describes the video editing module of FIG. 1 according toembodiments of the disclosure; and

FIG. 14 shows a flow chart describing a process of generating sportsgame highlight video based on excitement of gameplay implemented by thevideo processing system according to embodiments of the disclosure.

DESCRIPTION OF THE EMBODIMENTS

Referring now to the drawings, wherein like reference numerals designateidentical or corresponding parts throughout the several views.

The present disclosure provides a video processing system whichgenerates sports game highlight videos based on a point excitement modelwhich computes game excitement weights for tennis game elements (forexample, the quality of a shot or a rally at a certain point level ofthe tournament in terms of point, game, set, and match). Each gameexcitement weigh represents how exciting a sports game is/was withrespect to the corresponding game element. In some embodiments, the gameexcitement weights with respect to various game elements may be averagedto obtain the overall excitement weight at a point level. In othersports games, such as squash or badminton, the game element will bedifferent. The point excitement model may cover different levels of dataso is applicable from lowest level games where only scoring is availableup to games with full computer vision tracking in place. According toembodiments of the present disclosure, the video processing system maygenerate sports game highlight videos based on the point excitementmodel. In some embodiments, the video processing system may generatesports game highlight videos based on a winning probability model whichpredicts the winning probability at point level, game level, set level,match level, and tournament level.

According to embodiments of the present disclosure, the video processingsystem may generate sports game highlight videos based on a winningprobability model which predicts the winning probability at point level,game level, set level, match level, and tournament level. The deviationof the actual outcome of a sports game from the predicted winningprobability may represent how surprising and exciting the sports gameis/was. Embodiments of the disclosure can be used live (e.g. tohighlight the most exciting matches currently taking place in atournament) or retrospectively (e.g. to auto-generate a most interestingshort reel). Although embodiments of the disclosure are designed forgenerating sports game highlight videos for a tennis game, it isenvisaged that the various concepts of shot, rally, point, game, pointexcitement, and winning probability described herein may be equallyemployed for other types of sports games such as badminton, tabletennis, squash and volleyball.

FIG. 1 is a schematic block diagram illustrating a video processingsystem 100 for generating sports game highlight video based on qualityof gameplay according to embodiments of the disclosure. The videoprocessing system 100 may include a plurality of video cameras 101, 102,a computer vision tracking module 110, a shot event analyser 200, adatabase 600 for storing player historical statistics, a winningprobability model 300, a point excitement model 400, and a video editingmodule 500. The video cameras 101, 102 may be arranged to record videoimage data and audio data in a game play from different angles of thecourt, including the players, the officials such as chair umpire andline umpires, the coaches, the spectators and the commentators. AlthoughFIG. 1 shows only two video cameras, any number of video cameras isenvisaged. The computer vision tracking module 110 performs position andmovement tracking of the objects in the game court which may have animpact on the game scoring or reflect the performance of the players. Inthe example of tennis, the objects are in the court during the game suchas the players and tennis ball, by performing image recognition of theobjects in the video image data obtained from video cameras 101, 102. Inother words, the computer vision tracking module 110 identifies theelements in the video and then tracks the position and movement of theidentified objects through the video. In embodiments mathematicalmodelling may also be used to perform the tracking positions andmovements. The computer vision tracking module 110 outputs tracking datato the shot event analyser 200 for identifying the shot events in thevideo of the sports game, which represent different stages of a shot. Inthe example of tennis, a shot may consist of a series of events,starting with the tennis ball being hit by a player, the tennis ballpassing the net, the tennis ball landing on the court, and the tennisball being hit by the opponent. The shot event analyser 200 furtherpartitions the video of the sports game into video segments forindividual shots, based on the identified shot events. The shot eventanalyser 200 will be described later with reference to FIG. 2 .

The computer vision tracking module 110 also generates 3D tracking modelvideos from the tracking data to provide visual presentation of thetracking data, such as illustrating the trajectories of the object(e.g.: tennis ball) using the mathematical modelling. The 3D trackingmodel videos may be presented as an animation or an augmented realityvideo blending 3D objects into a real-life background. In the example oftennis, a 3D tracking model video illustrating a serve may include 3Dobjects representing the players and their positions, the players' footpositions, rackets, the tennis ball, the service box, the baseline, thetram lines and the net. The animation based on the 3D objects mayillustrate the tennis ball trajectory including the landing position,the change of player positions as well as body posture in response tothe tennis ball movement.

The winning probability model 300 estimates the winning probabilitieswith respect to different levels of a tournament such as a game, a set,and a match, based on the player historical statistics and shot eventmetadata respectively acquired from the database 600 and the shot eventanalyser 200, details of which will be described later with reference toFIG. 3 . The winning probabilities may be calculated before or after thesports game, or in real-time during the game based on the game progress,and output to the video editing module 500 for generating sports gamehighlight videos. The database structure of database 600 will bedescribed later with reference to FIG. 4 .

The point excitement model 400 evaluates the point excitement matrixwith respect to different elements of a sports game, based on the shotevent metadata acquired from the shot event analyser 200, details ofwhich will be described later with reference to FIG. 10 . The pointexcitement matrix comprises game excitement weights for game elements(for example, the quality of a shot or a rally at a certain point levelof the tournament in terms of point, game, set, and match) thatrepresents how exciting it is/was. The point excitement matrix may becalculated before or after a game, or in real-time during the sportsgame based on the game progress, and output to the video editing module500 for generating sports game highlight videos.

The video editing module 500 filters and sorts the video segments ofshot events obtained from the shot event analyser 200 based on the pointexcitement matrix and generates highlight videos to summarize the games.The video editing process can be performed according to user preferenceor a default setting, in regards of parameters such as video length,criteria of filtering and priority of sorting. Further details of thevideo editing module 500 will be described later with reference to FIG.13 .

FIG. 2 is a schematic block diagram illustrating the shot event analyser200 of FIG. 1 according to embodiments of the disclosure. The shot eventanalyser 200 includes a point state identifier 205, a game progressanalysis module 210, a shot analysis model 215, an unreturnabilityanalysis module 220, and a video segments splitting module 225. The shotevent analyser 200 performs analyses of the tennis game recordingsreceived from the video cameras 101, 102 based on the tracking datacalculated by the computer vision tracking module 110. The shot eventanalyses include identifying the moments of point state changes in therecordings by the point state identifier 205. In some embodiments, thepoint state information may be obtained based on an input of metadatafor the state of the game (for example, real-time score, scoring format,discipline) which comes from a scoring feed generated by a scoreboardcontroller. In some embodiments, the point state information may beobtained by analysing the image of the scoreboard in the video. Thepoint state information allows exciting points to be identified and winprobability to be calculated (e.g. the fifth set tiebreaker in a tennismatch), and will be described in further detail later with reference toFIGS. 3 and 10 . The shot event analyses also comprise tracking the gameprogress at the game progress analysis module 210 through determiningthe ending shots in a game, such as the winning shots and error shots,based on the tracking data and game rules. The game progress analysismodule 210 also recognizes and clusters a series of shots in a rally andgenerates data of the rally such as rally length, measured by the numberof shots in the rally or the absolute time taken by the rally. In themeantime, the shot analysis module 215 implements the indexing of eachshot in a match in respect of shot variety, shot quality, winning shotsand error shots, according to embodiments of the disclosure. Furtheranalysis on the quality of a shot is performed by the unreturnabilityanalysis module 220, which takes into account the difficulty for aplayer to return a particular shot. In some embodiments, theunreturnability of a shot indicates the likelihood that the opponentwill not successfully return that particular shot, and may be calculatedbased on information including: position of receiver, trajectory of theshot, speed of the shot, forward or backward stroke, and historical dataof receiver performance. The return improbability of the returning shotis also recorded based on the unreturnability of that particular shotprovided that it is successfully returned by the player. Althoughembodiments of the disclosure are designed for performing shot analysisin relation to a tennis game, it is envisaged that the various conceptsof winner shots, error shots, rally, shot variety, shot quality,unreturnability and return improbability described herein may be equallyemployed for other types of sports games such as badminton, tabletennis, squash and volleyball.

The video segments splitting module 225 extracts the game parts of thesports game recordings and splits them into video segments based on theresults of foregoing shot event analyses, namely, the shot eventmetadata. According to embodiments, the shot event metadata may includepoint state information, event timestamp, shot variety, rally length,winner (winning shots) information, errors information, shot quality,unreturnability and return improbability. Each of the video segments isindexed with corresponding shot event metadata.

FIG. 3 is a schematic block diagram illustrating the winning probabilitymodel 300 of FIG. 1 according to embodiments of the disclosure. Thewinning probability model 300 acquires player historical statistics andshot event metadata respectively from the database 600 and the shotevent analyser 200, and predicts the winning probability matrixincluding winning probabilities with respect to a point, game, set,match and tournament. According to embodiments of the disclosure, thewinning probability model 300 includes a game progress analysis module305, a point level probability model 310, a game level probability model315, a set level probability model 320, and a match level probabilitymodel 325.

According to embodiments of the disclosure, the game progress analysismodule 305 keeps track of the game progress based on the shot eventmetadata and game rules, and generates the point state and game statefor the corresponding video segment. For example, a video segment isidentified as relevant to a rally when the scoreboard is 30-15 in thethird game of the second set.

According to embodiments of the disclosure, the winning probabilitymodel 300 starts accessing the winning probability from viewing a sportsgame on a point by point level at the point level probability model 310.Once the winning probability for a point is calculated, the winningprobability model 300 proceeds to evaluate the winning probability for asports game that the point belongs to, based on the game levelprobability model 315. In some embodiments, the game level winningprobability may be computed before or after the sports game, or in areal-time manner during the sports game, based on the progress of thesports game provided by the game progress analysis module 305 and thewinning probability of the present point provided by the point levelprobability model 310.

The winning probability model 300 then evaluates the winning probabilityfor a set based on the set level probability model 320 and the winningprobabilities of the relevant sports games in the set calculated by thegame level probability model 315. In some embodiments, the set levelwinning probability may be computed before or after the sports game, orin a real-time manner during the sports game, based on the progress ofthe sports game provided by the game progress analysis module 305 andthe winning probability of the present game provided by the game levelprobability model 315.

Similarly, the winning probability model 300 evaluates the winningprobability for a match based on the match level probability model 325and the winning probabilities of the relevant sets in the matchcalculated by the set level probability model 320. In some embodiments,the match level winning probability may be computed before or after thesports game, or in a real-time manner during the sports game, based onthe progress of the sports game provided by the game progress analysismodule 305 and the winning probability of the present set provided bythe set level probability model 320.

The point level probability model 310, game level probability model 315,set level probability model 320 and match level probability model 325will be described in further detail later with reference to FIGS. 6-8 .

FIG. 4 illustrates a database structure for database 600 in FIG. 1 whichstores player historical statistics according to embodiments of thedisclosure. As previously described, the player historical statisticsmay be used for calculating winning probabilities according toembodiments of the disclosure.

In embodiments, the database 600 stores player historical statisticsincludes profile of the player such as age, current rank, best rank,best season in his/her career. The player historical statistics mayfurther include performance statistics of the player in previoustournaments or previous games in the present tournament. The performancestatistics may contain overall win rate and specific win rate withrespect to the surface type: hard, clay and grass; tournament level; thepoint state of a sports game, such as at a break point, a double fault,or a deuce; and various stress factors such as during a tie-break, orafter losing a set. The performance statistics may additionally includebreakdown of game statistics such as: serve won percentage, return wonpercentage, serve speeds, tie breaks won percentage, proportion ofdifferent types of shots/strokes and play styles, and the correspondingrate of winners, unforced errors and forced errors.

FIG. 5 illustrates an exemplary dataset of a winning probability matrixaccording to embodiments of the disclosure. As previously described, thewinning probability matrix may be used as the basis for the videoediting module 500 to generate sports game highlight videos.

In embodiments, the winning probability matrix includes video segmentserial number, player information, timestamp of the corresponding videosegments, scoreboard information, point level winning probability, gamelevel winning probability, set level winning probability, match winninglevel probability and tournament level winning probability.

FIG. 6 illustrates the point level probability model 310 in FIG. 3according to embodiments of the disclosure. The point level probabilitymodel 310 is constructed based on the tennis game rules for scoring apoint. For example, according to the game rules, after the player servesout, he or she is given a second chance to serve and he or she can stillscore one point if he or she wins the second serve. The point levelwinning probability is determined by the probabilities of winning afirst or second serve given that the serve landed in. The paths ofwinning a game point are represented by the shaded nodes in FIG. 7 , andthe probability of winning a point is mathematically represented as:

P(winning point)=(P ₃ ×P ₁)+(1−P ₃)×P ₄ ×P ₂

where

-   -   P₁ is the probability of winning the first serve    -   P₂ is the probability of winning the second serve    -   P₃ is the probability of first serve in    -   P₄ is the probability of second serve in

The probability at each node (i.e.: P₁, P₂, P₃ and P₄) of the pointlevel probability model 310 is calculated based on match differentialssuch as absolute strengths or relative strengths of the players in thesports game. The match differentials may be computed from the playerhistorical statistics, such as past ace rates and winning point ratesfor the relevant surface type (hard, clay or grass) stored in database600. In some embodiments, the probabilities for the same type of nodesmay be taken as the same by the probability model 310, for example, theprobability of serve in for a player may be taken as the same throughoutthe whole match. For a more accurate estimate of the probability, theprobability may be adjusted or scaled based on the game state (progressof the game) and player historical statistics corresponding to thatparticular game state, since the game state may affect winningconditions such as the fatigue, stress, and hence the performance of theplayer.

Although FIG. 6 shows a point level probability model for a tennis game,the construction of point level probability models for other sportsgames, such as badminton, table tennis, squash and volleyball todetermine winning probability is envisaged.

In some other embodiments, the probability values may be furtheradjusted to more accurately represent player levels and conditions for agiven match. For example, if the player is playing against an opponentwho has an above average success rate of winning a return point, theprobability of first serve win will be marked down accordingly.

FIG. 7 illustrates the game level probability model 315 in FIG. 3according to embodiments of the disclosure. The game level probabilitymodel 315 is constructed based on the tennis game rules for scoring agame. The game level probability model 315 is further built upon thepoint level probability model 310 described above. Specifically, theprobability calculated by the point level probability model 310 at eachpoint level is adopted by the node in the game level probability model315 corresponding to that point level.

The game level winning probability is determined by taking into accountwhich player starts serving the game and the probability of winning thegame can be chained to work out the probability of winning the pointfrom the given point state. The probabilities for all scenarios to winare added up from current game state. For example, the probability ofwinning the game by 40-0 is mathematically represented as:

P(winning a game by 40-0)=P(winning a point at 0-0)×P(winning a point at15-0)×P(winning a point at 30-0)×P(winning a point at 40-0)

Although FIG. 7 shows a game level probability model for a tennis game,the construction of game level probability models for other sportsgames, such as badminton, table tennis, squash and volleyball todetermine winning probability is envisaged.

FIG. 8 illustrates the set level probability model 320 in FIG. 3according to embodiments of the disclosure. The set level probabilitymodel 320 is constructed based on the tennis game rules for scoring aset. The set level probability model 320 is further built upon the gamelevel probability model 315 described above. Specifically, theprobability calculated by the game level probability model 315 at eachgame level is adopted by the node in the set level probability model 320corresponding to that game level.

The set level winning probability for a given scenario from the givengame state is determined by chaining the probability of the shot event(winning/losing a game) in each node along the path describing thescenario. The probabilities for all scenarios to win the set are thenadded up from the current game state.

The match level probability model 325 in FIG. 3 according to embodimentsof the disclosure is constructed in a similar manner as the set levelprobability model 320 based on the tennis game rules. The match levelprobability model 325 is further built upon the set level probabilitymodel 320 described above. Specifically, the probability calculated bythe set level probability model 320 at each set level is adopted by thenode in the match level probability model 325 corresponding to that setlevel.

The match level winning probability for a given scenario from the givengame state is determined by chaining the probability of the set event(winning/losing a set) in each node along the path describing thescenario. The probabilities for all scenarios to win the set are thenadded up from the current game state.

FIG. 9 illustrates an exemplary neural network 900 of the winningprobability model according to embodiments of the disclosure. Inembodiments, the neural network 900 comprises an input layer of nodes905, a hidden layer of nodes 910 and an output layer of nodes 915.Although one hidden layer is shown in FIG. 9 , it is envisaged that theneural network may include a number of hidden layers. The shot eventmetadata and player historical statistics as described in FIG. 1 is fedinto neural network model 900 as training data set to perform training.Machine learning is implemented by comparing the output at the outputlayer and a manually calculated winning probability matrix based on thetraining data set. The parameters of the neural network nodes are thenadjusted by machine learning algorithms to align the output at theoutput layer and the manually calculated winning probability matrix foreach training data in the training data set. Once the training has beencompleted, the video processing system 100 can utilize the neuralnetwork model 900 for automatically calculating the winning probabilitymatrix.

FIG. 10 is a schematic block diagram illustrating the point excitementmodel 400 of FIG. 1 according to embodiments of the disclosure. Thepoint excitement model 400 evaluates point excitement matrix based onwinning probabilities at each point state provided by the winningprobability model 300 and shot event metadata generated by the shotevent analyser 200. According to embodiments of the disclosure, thepoint excitement model 400 comprises a shot variety excitement 405, arally length excitement module 410, a winners and errors excitementmodule 415, a final shot quality excitement module 420, an overall shotquality excitement module 425, a return improbability excitement module430, and an average unreturnability excitement module 435. According toembodiments of the disclosure, the point excitement model 400 may alsoreceive excitement flagging, which the user inserts via a user interfaceto mark the moments in the sports game video recordings, hence the videosegments of sports game events, that he considers exciting orinteresting.

According to embodiments of the disclosure, the shot variety excitementmodule 405 quantifies the excitement of shot by computing a shot varietyexcitement weight based on the types of shots played. For example, theshot variety excitement weight for a shot increases with the rarity ofthe shot, and also the likelihood that the shot could be a winner. Theshot variety excitement weight may comprise a shot type frequencyweighting with respect to frequency of winners. The shot type frequencyweighting may be the number of shots for a particular shot type pertotal number of shots played in a game, or in a recent period of time inthe player's career. Alternatively, the shot type frequency weightingmay be the complement or reciprocal of the number of shots for aparticular shot type per total number of shots played in a game, or in arecent period of time in the player's career.

The shot variety excitement weight may further include a rally-winningfrequency factor calculated based on the number of winning rallies pertotal number of rallies played in a game.

According to embodiments of the disclosure, the shot variety excitementweight may be expressed mathematically as:

${{shot}{variety}{excitement}{score}} = \frac{A \times C}{B}$

where A is the frequency of the shot type, e.g.: one for every 5 rallies

-   -   B is the percentage of the shot type among the total number of        shots played, e.g.: 10% of non-serve/return shots played    -   C is the percentage of win points when the shot type is played,        e.g.: the shot type wins a point 40% of the time when it is        played

For instances, the shot variety excitement weight of a player forgroundstrokes, smash, volley may be 0.72, 1 and 1.69 respectively.

According to embodiments of the disclosure, the rally length excitementmodule 410 quantifies the excitement of shot by computing a rally lengthexcitement weight based on how many shots are played in a rally. Forexample, the rally length excitement weight may be calculated bymodified log function which is scaled using range of observed values.According to embodiments of the disclosure, the rally length excitementweight may be expressed mathematically as:

${{rally}{length}{excitement}{score}} = {{\log\left( \frac{{rally}{length}}{2} \right)} + 1}$

According to embodiments of the disclosure, the winners and errorsexcitement module 415 quantifies the excitement of shot by evaluating awinners and errors excitement weight according to how exciting a winner,a forced error, or an unforced error is when observed based on therecorded frequency. The winners and errors excitement module 415 mayreceive the number of winners, forced errors and unforced errors of aplayer in a game, or in a recent period of time of the player's career,and generate weights from the observed shots using returnability fromthe winners and errors inference work as multiplier. According toembodiments of the disclosure, the winners and errors excitement weightmay be expressed mathematically as:

${{winners}{excitement}{score}} = \frac{{overall}{average}{returnability}}{{overaaverage}{returnability}_{winners}}$${{forced}{errors}{excitement}{score}} = \frac{{overall}{average}{returnability}}{{average}{returnability}_{{forced}{errors}}}$${{unforced}{errors}{excitement}{score}} = \frac{{overall}{average}{returnability}}{{average}{returnability}_{{unforced}{errors}}}$

where

overall average returnability=(averagereturnability_(winners)×distribution of outcome_(winners))+(averagereturnability_(forced errors)×distribution ofoutcome_(forced errors))+(averageretunability_(unforced errors)×distribution ofoutcome_(unforced errors))

Table 1 illustrates examples of the numerical values for the calculationof winners and errors excitement weight according to embodiments of thedisclosure.

TABLE 1 (Average returnability) × Average Distribution (distribution ofExcitement returnability of outcomes outcomes) weight Winners 12% 34%0.0408 2.367 Forced errors 25% 32% 0.0800 1.136 Unforced 48% 34% 0.16320.592 errors Overall 0.2840 average returnability

According to embodiments of the disclosure, the final shot qualityexcitement module 420 quantifies the excitement of a shot by evaluatinga final shot quality excitement weight based on how exciting the lastshot of a rally was. For example, the final shot quality excitementmodule 420 may multiply the unreturnability value of a final shot withthe excitement weight from winners and errors excitement module 420depending on the type of the final shot as a winner, a forced error oran unforced error. Accordingly, a complex shot which is a winner mayhave a higher final shot quality excitement weight. The final shotquality excitement weight may be expressed mathematically as:

final shot quality excitement score=unreturnability_(final shot)×winnersand errors excitement score_(final shot)

According to embodiments of the disclosure, the overall shot qualityexcitement module 425 quantifies the excitement of shot by evaluating anoverall shot quality excitement weight based on how exciting therelevant shot was. The overall shot quality excitement module 425 maytake into account the shot event metadata acquired from the shot eventanalyser 200, including: how good is the placement of the shot, theorigin court zone and destination bounce zone.

The overall shot quality excitement weight may further be calculatedbased on the aggressiveness of the shot, for example, a volley may beregarded as more aggressive than a control type shot such as a dropshot. The overall shot quality excitement module 425 may additionallyevaluate the overall shot quality excitement weight based on thecomputer vision tracking data generated by the computer vision trackingmodule 110, such as the speed injection from the previous shot and thearc of the shot (e.g.: the flatter the arc the better the shot).

According to embodiments of the disclosure, the overall shot qualityexcitement module 430 quantifies the excitement of shot by evaluating areturn improbability excitement weight based on how exciting a returnshot is. In embodiments, the overall shot quality excitement module 430may calculate the return improbability excitement weight by multiplyingthe unreturnability value of the incoming shot with the unreturnabilityvalue of the return shot. As a result, the return improbabilityexcitement may have a higher value if an incoming shot has a highunreturnability value and meanwhile it is returned at a highunreturnability value. The return improbability excitement weight may beexpressed mathematically as:

return improbabilty excitementscore=unreturnability_(previous incoming shot)×unreturnability_(current return shot)

According to embodiments of the disclosure, the average unreturnabilityexcitement module 435 quantifies the excitement of shot by evaluating anaverage unreturnability excitement weight based on how exciting a rallyis. In embodiments, the average unreturnability excitement module 435may calculate the average unreturnability excitement weight by summingup the unreturnability values of all shots within the relevant rally anddividing the sum by the number of shots. For instance, a rally may havea higher average unreturnability excitement weight if the players playhigher proportion of unreturnable shots. The average unreturnabilityexcitement weight may be expressed mathematically as:

${{average}{unreturnability}{excitement}{score}} = \frac{\sum{{unreturnability}{of}{each}{shot}{in}a{rally}}}{{total}{number}{of}{shots}{in}a{rally}}$

According to embodiments of the disclosure, the point excitement model400 outputs a point excitement matrix consisting of the variousexcitement weight described above. In some embodiments, an overallexcitement weight for a certain part of a tournament may be calculated,for example, by averaging the various excitement weights of the shotswithin that particular portion of the tournament. In some embodiments,the user may set preference on the weighting of the various excitementweight such that the overall excitement weight may be calculated withemphasis on certain desired excitement factors, for example, final shotquality.

According to embodiments of the disclosure, the point excitement model400 may further calculate the point excitement matrix based onexcitement flags added to the timeline of the video recordings by usermarking the exciting moments in the sports game. For example, a user mayinsert a flag for a video segment if he finds the video segmentparticularly exciting. The point excitement model 400 may then calculatethe overall excitement weight by adding the weighting based on the userflagging, in addition to the various excitement weights as describedabove.

According to embodiments of the disclosure, the point excitement model400 may further calculate the point excitement matrix based on analysisof commentator reaction in the video recordings. For example, the pointexcitement model 400 may calculate the overall excitement weight byadding the weighting based on the commentator reaction including thegesture, facial expression, and body movement of the commentator, andthe sound level, pitch and transcript of the commentary, in addition tothe various excitement weights as described above.

According to embodiments of the disclosure, the point excitement model400 may further calculate the point excitement matrix based on analysisof spectator reaction in the video recordings. For example, the pointexcitement model 400 may calculate the overall excitement weight byadding the weighting based on the spectator reaction including the soundlevel and length of crowd cheering, and the gesture, facial expression,and body movement of the spectators, in addition to the variousexcitement weights as described above.

According to embodiments of the disclosure, the point excitement model400 may further calculate the point excitement matrix based on audioanalysis of an audio track in the video of the sports game. For example,the point excitement model 400 may calculate the overall excitementweight by adding the weighting based on the sound level and pitch of thecommentator audio, spectator audio, or player audio.

FIG. 11 illustrates an exemplary dataset of a point excitement matrixaccording to embodiments of the disclosure. As previously described, thepoint excitement matrix may be used as the basis for the video editingmodule 500 to generate sports game highlight videos.

In embodiments, the point excitement matrix includes video segmentserial number, player information, timestamp of the corresponding videosegments, scoreboard information, set-match excitement weight, playerstrengths excitement weight, shot variety excitement weight, rallylength excitement, winners/errors excitement weight, final shot qualityexcitement weight, generic quality excitement weight, returnimprobability excitement weight, and average unreturnability excitementweight.

FIG. 12 illustrates an exemplary neural network 1200 of the pointexcitement model according to embodiments of the disclosure. Inembodiments, the neural network 1200 comprises an input layer of nodes1205, a hidden layer of nodes 1210 and an output layer of nodes 1215.Although one hidden layer is shown in FIG. 12 , it is envisaged that theneural network may include a number of hidden layers. The shot eventmetadata, the winning probability at each point state and userexcitement flagging as described in FIG. 10 is fed into neural networkmodel 1200 to perform training. The training data may further includemanually evaluated point excitement matrix for conducting supervisedtraining. Once the training has been done, the video processing system100 can utilize the neural network model 1200 for automaticallyevaluating the point excitement matrix.

FIG. 13 is a schematic block diagram illustrating the video editingmodule 500 of FIG. 1 according to embodiments of the disclosure. Thevideo editing module 500 generates highlight videos based on the shotevent metadata, point excitement matrix, video segments of shot eventsand 3D tracking model videos respectively received from the shot eventanalyser 200, point excitement model 400, shot event analyser 200, andcomputer vision tracking module 110. According to embodiments of thedisclosure, the video editing module 500 may comprise a video segmentsfiltering module 505 and a video segments ranking module 510 whichorganizes the video segments by filtering and ranking them based on theshot event metadata and point excitement matrix according to prioritiesand preferences by default or set by the user. By ranking the videosegments based on the point excitement matrix, the video segmentscorresponding to games with more exciting game elements will have ahigher priority to be added to the sports game highlight videos. Forexample, the user may set the filtering criteria as all service aces ofa certain player in a tournament, sort the segments according tounreturnability and ball speed, and view the top 10 best played and mostexciting service aces.

In some embodiments, the video editing module 500 organizes the videosegments based on the shot event metadata and the winning probabilitymatrix. Specifically, video segments are ranked according to thedeviations of the actual outcomes of the sports game from thecorresponding probabilities in the winning probability matrix. Forexample, a game played by a player with a low probability of winning thesports game at that game state is considered as surprising and excitingif the player turns out winning the sports game (e.g.: if the winningprobability is 0.35 and the outcome is 1 which represents winning, thedeviation is 1−0.35=0.65). By ranking the video segments based on thedeviations of the actual outcomes from the winning probabilities, thevideo segments corresponding to the games with more surprising outcomewill have a higher priority to be added to the sports game highlightvideos.

The video editing module 500 further comprises a video merging module515 which generates the highlight videos by concatenating the organizedvideo segments and insert the corresponding 3D tracking model videosaccording to priorities and preferences of game excitement weights bydefault or set by the user. For instance, the user may set thepreference of adding 3D tracking model videos for all faults and outs inthe sports game highlight videos. In some embodiments, a 3D trackingmodel video may be merged with the original sports game video byoverlaying as a sub-display (Picture-in-Picture) in the game videosegment corresponding to the same shot, to form the sports gamehighlight video. In some embodiments, a 3D tracking model video may bemerged with the original game video by adding as full screen displayafter the game video segment corresponding to the same shot.

FIG. 14 is a flowchart describing a computerized process 1400 forgenerating sports game highlight video based on excitement of gameplayimplemented by the video processing system 100 according to embodimentsof the disclosure. Process 1400 begins at step 1405, where the videoprocessing system 100 receives video of the sports game from at leastone image capture devices. The image capture devices may be triangulatedin order to capture video for object tracking analysis. At step 1410,the video processing system 100 may perform object tracking analysis onthe video to generate tracking data providing object trajectory andplayer position information.

At step 1415, the video processing system 100 may identify shot eventsin the video of the sports game based on the tracking data. At step1420, the video processing system 100 may extract video segments of theidentified shot events from the video of the sports game. At step 1425,the video processing system 100 may generate shot event metadata forindexing the video segments. At step 1430, the video processing system100 may calculate a point excitement matrix by a point excitement modelbased on the shot event metadata. At step 1435, the video processingsystem 100 may generate a sports game highlight video based on the pointexcitement matrix.

According to embodiments of the disclosure, the application of thesports game highlight videos generated by the video processing system100 based on excitement of gameplay may include broadcasting scenariossuch as TV broadcasting, live streaming and game reporting of atournament, in which the sports game highlight videos may provide asummary with respect to a game, a match, or different levels of thetournament.

According to embodiments of the disclosure, the application of thesports game highlight videos generated by the video processing system100 based on excitement of gameplay may include coaching scenarios suchas providing evaluation of game performance, analyses of players forgame tactics and strategies, and insights for coaching. In someembodiments, the sports game highlight videos may be used to preparetraining materials by capturing the footage of professional playersregarding particular skills chosen by the user.

By adopting the embodiments of the disclosure in a video processingsystem to automatically generate sports game highlight videos based on apoint excitement model, the data transmission and storage of videorecordings in relation to a tournament can be significantly reduced. Theembodiments of the disclosure further provide an efficient method forgenerating sports game highlight videos in a real-time manner which inthe meantime has improved accuracy in representing the most excitingparts of the sports games.

Numerous modifications and variations of the present disclosure arepossible in light of the above teachings. It is therefore to beunderstood that within the scope of the appended claims, the disclosuremay be practiced otherwise than as specifically described herein.

In so far as embodiments of the disclosure have been described as beingimplemented, at least in part, by software-controlled data processingapparatus, it will be appreciated that a non-transitory machine-readablemedium carrying such software, such as an optical disk, a magnetic disk,semiconductor memory or the like, is also considered to representembodiments of the present disclosure.

It will be appreciated that the above description for clarity hasdescribed embodiments with reference to different functional units,circuitry and/or processors. However, it will be apparent that anysuitable distribution of functionality between different functionalunits, circuitry and/or processors may be used without detracting fromthe embodiments.

Described embodiments may be implemented in any suitable form includinghardware, software, firmware or any combination of these. Describedembodiments may optionally be implemented at least partly as computersoftware running on one or more data processors and/or digital signalprocessors. The elements and components of any embodiment may bephysically, functionally and logically implemented in any suitable way.Indeed, the functionality may be implemented in a single unit, in aplurality of units or as part of other functional units. As such, thedisclosed embodiments may be implemented in a single unit or may bephysically and functionally distributed between different units,circuitry and/or processors.

Although the present disclosure has been described in connection withsome embodiments, it is not intended to be limited to the specific formset forth herein. Additionally, although a feature may appear to bedescribed in connection with particular embodiments, one skilled in theart would recognize that various features of the described embodimentsmay be combined in any manner suitable to implement the technique.

Embodiments of the present technique can generally be described by thefollowing numbered clauses:

1. A method for generating a sports game highlight video based onexcitement of gameplay, comprising the steps of:

-   -   receiving video of the sports game from at least one image        capture device;    -   performing object tracking analysis on the video to generate        tracking data providing object trajectory and player position        information;    -   identifying shot events in the video of the sports game based on        said tracking data;    -   extracting video segments of the identified shot events from the        video of the sports game;    -   generating shot event metadata for indexing the video segments;    -   calculating a point excitement matrix by a point excitement        model based on said shot event metadata; and    -   generating a sports game highlight video based on said point        excitement matrix.

2. The method for generating a sports game highlight video according toclause 1, wherein the step of calculating the point excitement matrixcomprises evaluating a game excitement weight with respect to a shotbased on the occurrence of a shot category and likelihood of a winnershot in a rally.

3. The method for generating a sports game highlight video according toany preceding clause, wherein the step of calculating of the pointexcitement matrix comprises evaluating a game excitement weight withrespect to a rally based on rally length.

4. The method for generating a sports game highlight video according toclause 3, wherein the rally length is measured by the number of shots inthe rally or the absolute time taken by the rally. 5. The method forgenerating a sports game highlight video according to clause 1, whereinthe step of calculating the point excitement matrix comprises evaluatinga game excitement weight with respect to a winner and an error by theplayer based on frequency of winner, or frequency of forced errors orfrequency of unforced errors.

6. The method for generating a sports game highlight video according toany preceding clause, wherein the step of calculating the pointexcitement matrix comprises evaluating a game excitement weight withrespect to a rally based on the quality of the final shot in a rally.

7. The method for generating a sports game highlight video according toclause 6, wherein the quality of the final shot in a rally is calculatedby multiplying unreturnability of the final shot in a rally with a gameexcitement weight with respect to a winner and an error by the playerbased on frequency of winner, or frequency of forced errors or frequencyof unforced errors

8. The method for generating a sports game highlight video according toany preceding clause, wherein the step of calculating the pointexcitement matrix comprises evaluating a game excitement weight withrespect to a sports game based on overall shot quality in the sportsgame.

9. The method for generating a sports game highlight video according toany preceding clause, wherein the step of calculating the pointexcitement matrix comprises evaluating a game excitement weight withrespect to a return shot based on return improbability.

10. The method for generating a sports game highlight video according toclause 9, wherein the return improbability is calculated by multiplyingincoming shot unreturnability with return shot unreturnability.

11. The method for generating a sports game highlight video according toany preceding clause, wherein the step of calculating the pointexcitement matrix comprises evaluating a game excitement weight withrespect to a rally based on average rally unreturnability across allshots within the rally.

12. The method for generating a sports game highlight video according toany of clauses 7, 10 or 11, wherein the unreturnability of a shot iscalculated based on the position of player receiving the shot,trajectory of the shot, speed of the shot, forward or backward stroke,or historical data of player performance.

13. The method for generating a sports game highlight video according toany preceding clause, wherein the step of calculating the pointexcitement matrix comprises evaluating a game excitement weight withrespect to point state information, wherein the point state informationis obtained from a scoring feed or by analysing the video of the sportsgame.

14. The method for generating a sports game highlight video according toany preceding clause, wherein the calculating of the point excitementmatrix is further based on excitement flags added to the video of thesports game by a user marking exciting moments in the sports game.

15. The method for generating a sports game highlight video according toany preceding clause, wherein the calculating of the point excitementmatrix is further based on analysis of commentator reaction in the videoof the sports game.

16. The method for generating a sports game highlight video according toany preceding clause, wherein the calculating of point excitement matrixis further based on analysis of spectator reaction in the video of thesports game.

17. The method for generating a sports game highlight video according toany preceding clause, wherein the calculating of point excitement matrixis further based on analysis of an audio track in the video of thesports game, including at least one of commentator audio, spectatoraudio, or player audio.

18. The method for generating a sports game highlight video according toany preceding clause, wherein the step of calculating the pointexcitement matrix comprises using a machine learning algorithm trainedby historical data including shot event metadata.

19. The method for generating a sports game highlight video according toany preceding clause, wherein the step of performing video editingfurther comprises:

-   -   filtering and ranking the video segments according to at least        one game excitement weight in said point excitement matrix; and    -   generating a sports game highlight video by concatenating the        filtered and ranked video segments.

20. The method for generating a sports game highlight video according toclause 19, wherein the step of performing video editing furthercomprises:

generating tracking model videos from tracking data; and

merging video segments with tracking model videos corresponding to thesame shot.

21. The method for generating a sports game highlight video according toany preceding clause, wherein the sports game is selected from the groupconsisting of tennis, badminton, table tennis, squash and volleyball.

22. A computer program product comprising computer readable instructionswhich, when loaded onto a computer, configure the computer to perform amethod according to any preceding clause.

23. An apparatus for generating a sports game highlight video,comprising circuitry configured to:

receive video of the sports game from at least one image capture device;

perform object tracking analysis on the video to generate tracking dataproviding object trajectory and player position information;

identify shot events in the video of the sports game based on saidtracking data;

extract video segments of the identified shot events from the video ofthe sports game;

generate shot event metadata for indexing the video segments;

calculate a point excitement matrix by a point excitement model based onsaid shot event metadata; and

generate a sports game highlight video based on said point excitementmatrix.

24. The apparatus for generating a sports game highlight video accordingto clause 23, wherein the circuitry is further configured so that thestep of calculating the point excitement matrix comprises evaluating agame excitement weight with respect to a shot based on the occurrence ofa shot category and likelihood of a winner shot in a rally.

25. The apparatus for generating a sports game highlight video accordingto any of clauses 23-24, wherein the circuitry is further configured sothat the step of calculating of the point excitement matrix comprisesevaluating a game excitement weight with respect to a rally based onrally length.

26. The apparatus for generating a sports game highlight video accordingto according to clause 25, wherein the rally length is measured by thenumber of shots in the rally or the absolute time taken by the rally.

27. The method for generating a sports game highlight video according toaccording to any of clauses 23-26, wherein the circuitry is furtherconfigured so that the step of calculating the point excitement matrixcomprises evaluating a game excitement weight with respect to a winnerand an error by the player based on frequency of winner, or frequency offorced errors or frequency of unforced errors.

28. The apparatus for generating a sports game highlight video accordingto according to any of clauses 23-27, wherein the circuitry is furtherconfigured so that the step of calculating the point excitement matrixcomprises evaluating a game excitement weight with respect to a rallybased on the quality of the final shot in a rally.

29. The apparatus for generating a sports game highlight video accordingto clause 28, wherein the quality of the final shot in a rally iscalculated by multiplying unreturnability of the final shot in a rallywith a game excitement weight with respect to a winner and an error bythe player based on frequency of winner, or frequency of forced errorsor frequency of unforced errors

30. The apparatus for generating a sports game highlight video accordingto any of clauses 23-29, wherein the circuitry is further configured sothat the step of calculating the point excitement matrix comprisesevaluating a game excitement weight with respect to a sports game basedon overall shot quality in the sports game.

31. The apparatus for generating a sports game highlight video accordingto any of clauses 23-30, wherein the circuitry is further configured sothat the step of calculating the point excitement matrix comprisesevaluating a game excitement weight with respect to a return shot basedon return improbability.

32. The apparatus for generating a sports game highlight video accordingto clause 31, wherein the return improbability is calculated bymultiplying incoming shot unreturnability with return shotunreturnability.

33. The apparatus for generating a sports game highlight video accordingto any of clauses 23-32, wherein the circuitry is further configured sothat the step of calculating the point excitement matrix comprisesevaluating a game excitement weight with respect to a rally based onaverage rally unreturnability across all shots within the rally.

34. The apparatus for generating a sports game highlight video accordingto any of clauses 29, 32 or 33, wherein the unreturnability of a shot iscalculated based on the position of player receiving the shot,trajectory of the shot, speed of the shot, forward or backward stroke,or historical data of player performance.

35. The apparatus for generating a sports game highlight video accordingto any of clauses 23-34, wherein the circuitry is further configured sothat the step of calculating the point excitement matrix comprisesevaluating a game excitement weight with respect to point stateinformation, wherein the point state information is obtained from ascoring feed or by analysing the video of the sports game.

36. The apparatus for generating a sports game highlight video accordingto any of clauses 23-35, wherein the circuitry is further configured sothat the calculating of the point excitement matrix is further based onexcitement flags added to the video of the sports game by a user markingexciting moments in the sports game.

37. The apparatus for generating a sports game highlight video accordingto any of clauses 23-36, wherein the circuitry is further configured sothat the calculating of the point excitement matrix is further based onanalysis of commentator reaction in the video of the sports game.

38. The apparatus for generating a sports game highlight video accordingto any of clauses 23-37, wherein the circuitry is further configured sothat the calculating of point excitement matrix is further based onanalysis of spectator reaction in the video of the sports game.

39. The apparatus for generating a sports game highlight video accordingto any of clauses 23-38, wherein the calculating of point excitementmatrix is further based on analysis of an audio track in the video ofthe sports game, including at least one of commentator audio, spectatoraudio, or player audio.

40. The apparatus for generating a sports game highlight video accordingto any of clauses 23-39, wherein the circuitry is further configured sothat the step of calculating the point excitement matrix comprises usinga machine learning algorithm trained by historical data including shotevent metadata.

41. The apparatus for generating a sports game highlight video accordingto any of clauses 23-40, wherein the circuitry is further configured sothat the step of generating a sports game highlight video furthercomprises:

-   -   filtering and ranking the video segments according to at least        one game excitement weight in said point excitement matrix; and    -   generating a sports game highlight video by concatenating the        filtered and ranked video segments.

42. The apparatus for generating a sports game highlight video accordingto clause 41, wherein the circuitry is further configured so that thestep of generating a sports game highlight video further comprises:

generating tracking model videos from tracking data; and

merging video segments with tracking model videos corresponding to thesame shot.

43. The apparatus for generating a sports game highlight video accordingto any of clauses 23-42, wherein the sports game is selected from thegroup consisting of tennis, badminton, table tennis, squash andvolleyball.

What is claimed is:
 1. A method for generating a sports game highlightvideo based on excitement of gameplay, comprising the steps of:receiving video of the sports game from at least one image capturedevice; performing object tracking analysis on the video to generatetracking data providing object trajectory and player positioninformation; identifying shot events in the video of the sports gamebased on said tracking data; extracting video segments of the identifiedshot events from the video of the sports game; generating shot eventmetadata for indexing the video segments; calculating a point excitementmatrix by a point excitement model based on said shot event metadata;and generating a sports game highlight video based on said pointexcitement matrix.
 2. The method for generating a sports game highlightvideo according to claim 1, wherein the step of calculating the pointexcitement matrix comprises evaluating a game excitement weight withrespect to a shot based on the occurrence of a shot category andlikelihood of a winner shot in a rally.
 3. The method for generating asports game highlight video according to claim 1, wherein the step ofcalculating of the point excitement matrix comprises evaluating a gameexcitement weight with respect to a rally based on rally length.
 4. Themethod for generating a sports game highlight video according to claim3, wherein the rally length is measured by the number of shots in therally or the absolute time taken by the rally.
 5. The method forgenerating a sports game highlight video according to claim 1, whereinthe step of calculating the point excitement matrix comprises evaluatinga game excitement weight with respect to a winner and an error by theplayer based on frequency of winner, or frequency of forced errors orfrequency of unforced errors.
 6. The method for generating a sports gamehighlight video according to claim 1, wherein the step of calculatingthe point excitement matrix comprises evaluating a game excitementweight with respect to a rally based on the quality of the final shot ina rally.
 7. The method for generating a sports game highlight videoaccording to claim 6, wherein the quality of the final shot in a rallyis calculated by multiplying unreturnability of the final shot in arally with a game excitement weight with respect to a winner and anerror by the player based on frequency of winner, or frequency of forcederrors or frequency of unforced errors
 8. The method for generating asports game highlight video according to claim 1, wherein the step ofcalculating the point excitement matrix comprises evaluating a gameexcitement weight with respect to a sports game based on overall shotquality in the sports game.
 9. The method for generating a sports gamehighlight video according to claim 1, wherein the step of calculatingthe point excitement matrix comprises evaluating a game excitementweight with respect to a return shot based on return improbability. 10.The method for generating a sports game highlight video according toclaim 9, wherein the return improbability is calculated by multiplyingincoming shot unreturnability with return shot unreturnability.
 11. Themethod for generating a sports game highlight video according to claim1, wherein the step of calculating the point excitement matrix comprisesevaluating a game excitement weight with respect to a rally based onaverage rally unreturnability across all shots within the rally.
 12. Themethod for generating a sports game highlight video according to claim7, wherein the unreturnability of a shot is calculated based on theposition of player receiving the shot, trajectory of the shot, speed ofthe shot, forward or backward stroke, or historical data of playerperformance.
 13. The method for generating a sports game highlight videoaccording to claim 1, wherein the step of calculating the pointexcitement matrix comprises evaluating a game excitement weight withrespect to point state information, wherein the point state informationis obtained from a scoring feed or by analysing the video of the sportsgame.
 14. The method for generating a sports game highlight videoaccording to claim 1, wherein the calculating of the point excitementmatrix is further based on excitement flags added to the video of thesports game by a user marking exciting moments in the sports game. 15.The method for generating a sports game highlight video according toclaim 1, wherein the calculating of the point excitement matrix isfurther based on at least one of analysis of commentator reaction in thevideo of the sports game, analysis of spectator reaction in the video ofthe sports game, or further based on analysis of an audio track in thevideo of the sports game, wherein the audio track includes at least oneof commentator audio, spectator audio, or player audio.
 16. The methodfor generating a sports game highlight video according to claim 1,wherein the step of calculating the point excitement matrix comprisesusing a machine learning algorithm trained by historical data includingshot event metadata.
 17. The method for generating a sports gamehighlight video according to claim 1, wherein the step of performingvideo editing further comprises: filtering and ranking the videosegments according to at least one game excitement weight in said pointexcitement matrix; and generating a sports game highlight video byconcatenating the filtered and ranked video segments.
 18. The method forgenerating a sports game highlight video according to claim 17, whereinthe step of performing video editing further comprises: generatingtracking model videos from tracking data; and merging video segmentswith tracking model videos corresponding to the same shot.
 19. Themethod for generating a sports game highlight video according to claim1, wherein the sports game is selected from the group consisting oftennis, badminton, table tennis, squash and volleyball.
 20. Anon-transitory computer readable medium storing a computer readableinstructions that, when loaded onto a computer, configure the computerto perform the method according to claim 1.